Indoor positioning (i.e. position finding, including floor detection) requires novel systems and solutions that are specifically developed and deployed for this purpose. The ‘traditional’ positioning technologies, which are mainly used outdoors, e.g. satellite and cellular positioning technologies, generally cannot deliver such performance indoors that would enable seamless and equal navigation experience in both environments. The required positioning accuracy (e.g. 2-3 m), coverage (e.g. ˜100%) and floor detection are challenging to achieve indoors with satisfactory performance levels with the systems and signals that were not designed and specified for the indoor use cases. Satellite-based radio navigation signals simply do not penetrate enough through the walls and roofs for adequate signal reception, and the cellular signals usually have too narrow bandwidth for accurate ranging by default.
Several indoor-dedicated solutions have been developed and commercially deployed during the past years, e.g. solutions based on pseudolites (Global Positioning System (GPS)-like short range beacons), ultra-sound positioning, BTLE signals (e.g. High-Accuracy Indoor Positioning, HAIP) and WiFi-Fingerprinting. What is typical to these solutions is that they require either deployment of totally new infrastructure (beacons, tags to name but a few examples) or manual exhaustive radio surveying of the buildings including all the floors, spaces and rooms. This is rather expensive and will take a considerable amount of time to build the coverage to the commercially expected level, which in some cases narrowed the potential market segment only to very thin customer base, e.g. for health care or dedicated enterprise solutions. Further, the diversity of these technologies makes it difficult to build a globally scalable indoor positioning solution, and the integration and testing will become complex if a large number of technologies needs to be supported in the consumer devices, such as smartphones.
For an indoor positioning solution to be commercially successful, that is, being globally scalable, having low maintenance and deployment costs, and offering acceptable end-user experience, the solution should be based on existing infrastructure in the buildings and on existing capabilities in the consumer devices. This leads to the conclusion that the indoor positioning is advantageously based on WiFi- and/or Bluetooth (BT)-technologies that are already supported in every smartphone, tablet, laptop and even in the majority of the feature phones. It is, thus, required to find a solution that uses the WiFi- and BT-radio signals in such a way that makes it possible to achieve e.g. 2-3 m horizontal positioning accuracy, e.g. close to 100% floor detection with the ability to quickly build the global coverage for this approach.
Floor detection can be based, at least in part, on altitude values, which are known to be estimated by GPS. However, such altitude estimation have very low accuracy (e.g. +−50 m), which is not sufficient for floor detection. Accurate altitude for WiFi-radio map creation, which can be used for indoor positioning respectively navigation may be desirable.
Huge volumes of measurements data could be harvested via crowd-sourcing if the consumer devices were equipped with the necessary functionality to enable the data collection as a background process, naturally with the end-user consent. It could also be possible to use volunteers to survey the buildings in exchange of reward or recognition and get the coverage climbing up globally in the places and venues important for the key customers. However, the technical challenges related to the harvesting, processing, redundancy, ambiguity and storing the crowd-sourced data need to be understood and solved first, before the WiFi-radio map creation can be based on the fully crowd-source data.